MES development for optimal distribution of fuel and energy resources in blast-furnace production

Гурин, И. А.; Spirin, N. А.; Лавров, В. В.

doi:10.1109/icieam.2017.8076425

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…Although the number of neurons in SDSO-RBFNN K is always in the process of dynamic change, it eventually changes dynamically within a certain range. After longterm experiments, we find that K tends to be in the order of 2 10 under long-term stable normal working conditions, but when abnormal working conditions occur, the number of K increases greatly and decreases after the abnormal working conditions end.…”

Section: Structure Dynamic Self-optimizing Rbf Neural Network (Sdso-r...mentioning

confidence: 86%

“…The blast furnace stockline is a key control parameter in the ironmaking process. Accurate and continuous real-time blast furnace stockline information is the key to adjusting the blast furnace production process, achieving precise control of blast furnace material charging, and stabilizing blast furnace working conditions [2]. A blast furnace is a typical black box and a large metallurgical reactor [3].…”

Section: Introductionmentioning

confidence: 99%

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High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics

Liu

Chen

Gui

et al. 2022

Sensors

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The real-time, continuity, and accuracy of blast furnace stockline information are of great significance in reducing energy consumption and improving smelting efficiency. However, the traditional mechanical measurement method has the problem of measuring point discontinuity, while the radar measurement method exhibits problems such as weak anti-interference ability, low accuracy, and poor stability. Therefore, a high-dimensional, spatial feature stockline detection method based on the maximum likelihood radial basis function model (MLRBFM) and structural dynamic self-optimization RBF neural network (SDSO-RBFNN) is proposed. Firstly, the discrete time series joint partition method is used to extract the time dimension periodic features of the blast furnace stockline. Based on MLRBFM, the high-dimensional spatial features of the stockline are then obtained. Finally, an SDSO-RBFNN is constructed based on an eigen orthogonal matrix and a right triangular matrix decomposition (QR) direct clustering algorithm with spatial–temporal features as input, so as to obtain continuous, high-precision stockline information. Both the simulation results and industrial validation indicate that the proposed method can provide real-time and accurate stockline information, and has great practical value for industrial production.

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Section: Structure Dynamic Self-optimizing Rbf Neural Network (Sdso-r...mentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics

Liu

Chen

Gui

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The iron and steel industry is a major energy consumer and carbon emitter. As the most critical equipment for iron and steel smelting, blast furnaces account for about 60%~70% of the comprehensive energy consumption and cost of iron and steel [ 1 ], and CO 2 emissions account for more than 90% [ 2 ]. Blast furnace burden level is a key parameter of blast furnace production [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features

Yan-li

Chen

Gui

et al. 2022

Sensors

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Real-time, continuous and accurate blast furnace burden level information is of great significance for controlling the charging process, ensuring a smooth operation of a blast furnace, reducing energy consumption and emissions and improving blast furnace output. However, the burden level information measured by conventional mechanical stock rods and radar probes exhibit problems of weak anti-interference ability, large fluctuations in accuracy, poor stability and discontinuity. Therefore, a space-time fusion prediction and detection method of burden level based on a long-term focus memory network (LFMN) and an efficient structure self-tuning RBF neural network (ESST-RBFNN) is proposed. First, the space dimensional features are extracted by the space regression model based on radar data. Then, the LFMN is designed to predict the burden level and extract the time dimensional features. Finally, the ESST-RBFNN based on a proposed fast eigenvector space clustering algorithm (ESC) is constructed to obtain reliable and continuous burden level information with high accuracy. Both the simulation results and industrial verification indicate that the proposed method can provide real-time and continuous burden level information in real-time, which has great practical value for industrial production.

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Regression Testing of Information-Modeling Systems for Solving Technological Problems in Blast-Furnace Production

Лавров

Spirin

Гурин

2018

2018 International Russian Automation Conference (RusAutoCon)

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MES development for optimal distribution of fuel and energy resources in blast-furnace production

Cited by 4 publications

References 1 publication

High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics

High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics

Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features

Regression Testing of Information-Modeling Systems for Solving Technological Problems in Blast-Furnace Production

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